Related fields include artificial intelligence in management of a knowledge base and tracking the position of an object relative to a reference system.
Mobile social networking expanded into the dimensions of “real life” when (1) global positioning satellite (GPS) services became common on cellular phones, smart-phones, personal digital assistants (PDAs) and tablet computers (collectively “mobile devices”), and (2) social-network service providers (SNSPs) became able to collect location data from member devices. SNSP software (usually, but not always, on one or more servers) identifies co-located member devices by their transmitted GPS coordinates. “Co-location” might be measured as some sub-threshold proximity of the devices to each other, or to a venue whose coordinates are stored in a database. The software compares stored user profiles and social-network connections corresponding to the co-located devices and sends alerts to the devices upon detecting a correspondence: for instance, if two or more of the co-located devices belong to first- or second-degree contacts in the SNSP's network, or to people whose profiles share a common feature such as an interest or an affiliation of education or employment.
These “social-mobile-local” (SML) innovations are a boon to attendees trying to find each other at large events, to travelers visiting unfamiliar places, and to those simply finding themselves with “alone time” they would rather spend with others socially, professionally, or commercially. Early adopters were those able to afford sophisticated mobile devices, who had plentiful excess time and ambition to learn how to use the software and get the most out of their mobile devices. Mobile device price-per-capability decreased, yet many potential users who could benefit from using the software found it cumbersome to learn and execute.
In some cases, the user had to “check in” with the SNSP at every new location, which can be easily forgotten during a day of sales calls or an evening of multiple holiday parties. On the other hand, systems that constantly tracked a device's GPS coordinates quickly drained device batteries and triggered user privacy concerns. Generally, a user had to create a new profile just for the SML database, and could not import it when switching SNSPs.
Even within a single SNSP, one profile was not always enough. Increasing employment mobility made constant professional networking a matter of economic survival—not only in traditionally connection-critical fields such as sales, public relations, and politics, but for everyone. Meanwhile, people still had social needs to be themselves, express themselves, and find companions like themselves. When both pursuits jumped to cyberspace, collisions were inevitable. Individuals were rejected for employment, and even fired from present jobs, solely for things said and done online by their “social selves.” Meanwhile, those who scrupulously limited their online presence to uncontroversial professionalism languished in social isolation unless blessed with a random serendipitous offline encounter with a coincidentally compatible other.
Typical work-arounds in the SML context included deactivation (“don't tell anyone I'm here”), preventing co-location alerts to specific other users (“don't tell my parents or my boss I'm here”) and dedicated multiple profiles (“tell people here that I'm a single guitar player, but not that I'm a tax auditor”). For the users, frequent impacts were more work (switching activation levels, profiles, and see-me lists several times a day) and missed connections.
Even where multiple work-arounds were needed, SML software for mobile devices (“SML apps”) evolved in ease of use as capable mobile devices became more available. A side effect of this was “alert flooding” or “notification spam.” Consider a user arriving at a major-league sports stadium. When SML was new, the user's mobile device would display a handful of alerts about connected or interest-sharing owners of sufficiently sophisticated devices who chose to subscribe to the SNSP and allow it to advertise their location. Later, half or more of the 10,000 fans at the stadium might have the same app on their devices and list an interest in the stadium's sport or team in their profile. Too much information may be better than none, but where does one begin to comb through 5,000 alerts to find the 5 or 6 upon which the user might really want to act?
In addition, the same uncertainties of meeting a stranger apply to SML as to online and non-technological situations: “Here is a stranger who thinks we would benefit by some kind of exchange, but (absent an recommendation by someone I trust) how do I know I can trust the stranger at least to the limits of the exchange? Meanwhile, the stranger may be wondering the same thing about me; how do I prove my suitability for the exchange without exposing my identity to misappropriation or misuse?” The criticality of these issues can vary widely depending on the nature of the exchange. For example, these questions do not loom very large in the sale of a snack, but quite large in the hiring of a home remodeler.
Therefore, a need exists for an SML environment where networking with professional decorum and social enjoyment can both be accommodated, separately if appropriate. Preferably, the environment would strike a palatable balance between the verification of credentials and the discouragement of identity theft and other misappropriation of personal details. Ideally, this functionality would not be laborious for the user to implement, nor would it demand frequent diligent attention to operate.